Why the majority of AI companies will end up as ‘roadkill’
As OpenAI pursues a public benefit structure and global expansion, the AI powerhouse’s influence cannot be understated. But where hundreds of new companies jump on the AI bandwagon every year to follow in the footsteps of the successful companies before them, 90% will inevitably fail.
Every few years a new hype cycle arises. Venture capitalists (VCs), under pressure to deliver exponential returns to their limited partners, are constantly looking for the next big thing. The 1990s saw the ‘dotcom’ boom; today it is AI.
While a few pioneering companies are actually innovating and developing groundbreaking AI technology, a perfect storm is brewing in the background. As the hype cycle intensifies, a host of players, both large and small, rush to capitalize on the trend, often without a clear understanding of the underlying technology or its potential applications.
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An accelerating trend
Several macro factors have converged to drive the rapid advancement of AI. The massive drop in the cost of computing power and storage, combined with ubiquitous internet access and decades of algorithmic research, have created the ideal conditions for the practical application of AI. While academics have long theorized about the potential of AI with massive data sets, the prohibitive cost of infrastructure has hindered their ability to turn these theories into reality.
However, a major shift occurred as the cost of supporting AI models became more affordable and accessible. This democratization of AI infrastructure paved the way for deep thinkers to showcase the possibilities and capabilities of the technology. The numerous potential business applications for AI led to a wave of interest and investment. For example, Google’s acquisition of DeepMind ten years ago for $400 million marked a pivotal moment.
This level of activity attracted hype investors, eager to capitalize on the AI boom and pouring significant amounts of money into the field. This influx of capital created a chaotic landscape as investors rushed to invest in AI companies. The hype cycle intensified, leading to unrest among investors who were not directly responsible for their investments. As with any market trend, a tsunami of collateral damage soon follows, leaving a trail of failed businesses and lost investments.
Why do so many fail?
Many AI startups will inevitably become market killers simply because they fail to build the foundations from the ground up. Eager to take advantage of the latest AI trends, they opt for front-end existing platforms such as GPT and Gemini. While this approach provides a quick route to market, it ultimately hinders innovation and differentiation in the long term. These companies completely bypass the critical phase of original thinking and experimentation. While they have impressive pitch decks and talented individuals, they lack the real lifeblood of any successful tech startup. Innovation must be ingrained in the DNA of the organization and must be the driving force behind every aspect of its activities.
At the heart of every successful AI startup is innovation. It is essential to bring something new to the market because simply replicating existing solutions does not provide a competitive advantage. To achieve this, companies must hire people who are naturally curious problem solvers and possess a unique perspective on the world.
Where innovation is your currency as a company, technology is your defense mechanism.
Is your business default dead or alive?
To build a sustainable AI business, profitability and margin are essential. Excessive funding from venture capital investments can actually hinder these goals, as startups become more concerned with burning cash than focusing on profitability. By putting their future in the hands of venture capital firms, startups risk becoming vulnerable to their investment decisions.
VC firms, acutely aware of the money from their LPs burning a hole in their pockets, are under enormous pressure to deploy capital quickly. This urgency often leads to hasty investments in early-stage startups, putting the same pressure on entrepreneurs to deliver results quickly. In the rush to launch AI products, many engineering teams are resorting to shortcuts, using pre-existing models like GPT as a foundation rather than investing in original research and development. Although this approach is appropriate, it leads to the mass creation of generic, undifferentiated products. As the market matures, consumers will increasingly gravitate toward innovative, unique offerings, leaving the AI roadkill behind.
Ultimately, VCs view companies as assets with the sole purpose of generating substantial returns; countless conveyor belts going to the same destination. If a startup’s growth potential falters – and the assembly line slows down – venture capital firms can quickly lose interest and start investing elsewhere. The constant pressure to achieve milestones and secure additional financing can lead to unsustainable business practices.
Paul Graham of Y Combinator famously describes companies as being in one of two states: “default dead” or “default alive.” Venture-backed companies often find themselves in the default state of being dead because they’re not making any money.
Emerged from the hype cycle as a success
Once we filter out the roadkill, the AI market will undergo a major transformation. What will remain are AI technologies that deliver real value and efficiency gains. Too many well-marketed “AI” companies are creating solutions for problems that don’t exist. AI will streamline processes, making it easier for companies to scale their operations. Contrary to the sci-fi image of AI as a human replacement, its real value lies in augmentation.
By providing workers with a robust exoskeleton, AI can enable companies to continue striving for feats currently considered beyond their reach. However, it is those AI companies that take the time to build out their own IP and proprietary technology that will stand the test of time and deliver the solutions that businesses have come to rely on.
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